We develop ideas on
Post-processing of partially corrected AO data is an Achilles' heel for the reconstruction of the PSF is an extremely complex task to accomplish, already in classical AO, let alone wide-field.
Currently we're developing software classes to
Turn-key AO requires robustness to operate 100% of the time at full performance. To that end new processing architectures are needed to tackle the curse of dimensionality (scaling with the fourth power of the telescope diameter D⁴) whilst making efficient use of combined LGS and NGS measurements in any of the WFAO modes. We are acquiring expertise and leading efforts in
We support the development of OOMAO, an Object-oriented Matlab Adaptive Optics library of Matlab R ⃝classes.
Objects from the different classes are assembled to perform the numerical modeling of Adaptive Optics systems. OOMAO can be seen as an extension of the Matlab R ⃝ language. Overloaded Matlab R ⃝ operators are used to propagate the wavefront through the system and to update object properties.
Public and private, version-controlled repositories can be found here
I strongly encourage the community to give it a try. It's licence-free and free-of-charge. As a future user, you're welcome to add to it and to become an official co-developer. Please drop me an email.
Atmospheric parameter identification is paramount for the runtime optimisation of tomography and the post-processing of AO telemetry.
As part of our running collaborations, we've hosted Masen Lamb, PhD student at UVic for 4 months (Mar-Jun 2016) to develop phase-diversity algorithms for AO systems calibration. These are now being applied to
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